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Contextual superpixel description for remote sensing image classification

机译:用于遥感影像分类的上下文超像素描述

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The performance of pattern classifiers depends on the separability of the classes in the feature space - a property related to the quality of the descriptors - and the choice of informative training samples for user labeling - a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We introduce a new scheme for superpixel description based on Bag of visual Words, which includes information from adjacent superpixels, and validate it by using two remote sensing images and several region descriptors as baselines.
机译:模式分类器的性能取决于特征空间中类的可分离性-与描述符的质量有关的属性-以及用于用户标记的信息培训样本的选择-该过程通常需要主动学习。当样本是来自遥感图像的超像素时,这项工作致力于提高描述符的质量。我们引入了一种基于视觉词袋的超像素描述新方案,该方案包括来自相邻超像素的信息,并通过使用两个遥感图像和几个区域描述符作为基线对其进行验证。

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